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cs.RO2026
Refine Actions If Needed: Adaptive Action Chunk Refinement for Robot Manipulation
Hanlin Liu, Zichen Zhou, Jiacheng Zhang +6
Computationally lightweight manipulation policies predict executable actions with low inference latency, but prediction errors can limit task success. Iterative generative policies…
cs.CV2026
Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model
Tao Lin, Yuxin Du, Jiting Liu +14
Vision-Language-Action models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action generation. However, they often s…
cs.RO2026
PALM: Progress-Aware Policy Learning via Affordance Reasoning for Long-Horizon Robotic Manipulation
Yuanzhe Liu, Jingyuan Zhu, Yuchen Mo +9
Recent advancements in vision-language-action (VLA) models have shown promise in robotic manipulation, yet they continue to struggle with long-horizon, multi-step tasks. Existing m…